Synthetic aperture radar (SAR) imaging is now being widely used to provide high-level, high-resolution images for surveillance, military, and law enforcement purposes. As the demand for more sophisticated intelligence information grows, coherent change detection (CCD) technology is increasingly being used for its ability to indicate change in a scene over time based upon SAR images of the scene. Furthermore, the automated extraction of intelligence from raw surveillance data has become necessary given the immense volume of such data being generated and the limited time available for analysts to process it. Automated image analysis is a promising solution to the problem of limited analyst manpower, but ordinary image processing techniques are often not robust enough to provide accurate interpretations of image data that may exhibit wide variation in image quality and the nature of scenes depicted. Existing techniques for identification of vehicle tracks in a scene generally focus on extraction of information directly from pixels in an image of the scene. These direct image processing techniques, though, are often prone to failure in high noise environments.